Gemini 2.5 Pro vs GLM 5.3

GLM 5.3 leads Gemini 2.5 Pro on coding, 70.6 to 19.1 on the Vibe Coding Index, and GLM 5.3 costs 2.3x less per output token ($4.40 vs $10.00 per million). Updated October 22, 2026; 56 benchmarks have a result for both.

Replace Gemini 2.5 Pro Replace GLM 5.3

Gemini 2.5 ProGoogle

Coding: 19.1
Overall: 41.8
Output $/M: $10.00

GLM 5.3Z.ai

Coding: 70.6
Overall: 67.9
Output $/M: $4.40

vs boards won of 56 shared

Gemini 2.5 Pro or GLM 5.3: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro.

Pick Gemini 2.5 Pro for work measured by MedCode (Vals AI) or LMArena Text: legal and government, where it leads.

Model AGemini 2.5 ProGoogle19.1/1003 independent benchmarks$0.64 per solved task Model BGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task

563444

Shared results: 56
Gemini 2.5 Pro only: 34
GLM 5.3 only: 44
Like-for-like categories: 8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

Coding
Vibe Coding Index, six public coding benchmarks
GLM 5.3
GLM 5.3 scores 70.6 against 19.1.
3 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 16.1.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 1.6.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 5.3 has a GPQA Diamond result, 88.1%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 has a MMLU Pro result, 86.8%.
Directional only
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1167.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3
Gemini 2.5 Pro $0.0063, GLM 5.3 $0.0036 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
Gemini 2.5 Pro $0.64, GLM 5.3 $0.36.
Published attempt cost and pass rate

At a glance

Coding index: Gemini 2.5 Pro 19.1 from 3 benchmarks, GLM 5.3 70.6 from 6 benchmarks.
Widest category: Agentic work, 49.5 points apart on average over 2 shared benchmarks.
Widest single gap: Vibe Code Bench (Vals AI): Gemini 2.5 Pro 0.4% against GLM 5.3 78.1%, 77.7 points apart on a 0 to 100 scale.
Output price: Gemini 2.5 Pro $10.00 against GLM 5.3 $4.40 per million output tokens, 2.3x apart.
Context window: Both take 1.05M tokens.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
Category	Gemini 2.5 Pro	GLM 5.3
Coding	0	9
Intelligence	0	2
Agentic work	0	2
Reasoning	0	6
Math	0	5
Knowledge	2	3
Web design	0	6
General	3	18
All shared boards	5	51

CodingLike-for-likeSix of these boards feed the Vibe Coding Index09

Coding benchmarks for Gemini 2.5 Pro and GLM 5.3
Benchmark	Gemini 2.5 Pro	GLM 5.3	Leader
SWE-bench Verified	53.6%	Not measured	Only Gemini 2.5 Pro measured
LiveBench coding	Not measured	82.8%	Only GLM 5.3 measured
LMArena WebDev	1226	1621	GLM 5.3
AA Coding Index	33.3	74.8	GLM 5.3
LiveCodeBench (Vals AI)	Not measured	80.5%	Only GLM 5.3 measured
SWE-bench Verified (Vals AI)	54.4%	95.4%	GLM 5.3
Terminal-Bench 4 (Vals AI)	Not measured	38.9%	Only GLM 5.3 measured
Vibe Code Bench (Vals AI)	0.4%	78.1%	GLM 5.3
Code migration (Vals AI)	Not measured	44.2%	Only GLM 5.3 measured
ProgramBench (Vals AI)	Not measured	1.5%	Only GLM 5.3 measured
IOI (Vals AI)	Not measured	68.4%	Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)	Not measured	71.5%	Only GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)	Not measured	20.0%	Only GLM 5.3 measured
SkillsBench (Vals AI)	Not measured	47.5%	Only GLM 5.3 measured
CyberBench (Vals AI)	Not measured	72.1%	Only GLM 5.3 measured
LiveCodeBench (Artificial Analysis run)	80.1%	Not measured	Only Gemini 2.5 Pro measured
SciCode	46.3%	59.0%	GLM 5.3
Terminal-Bench Hard (Artificial Analysis run)	26.5%	Not measured	Only Gemini 2.5 Pro measured
Terminal-Bench	32.6%	Not measured	Only Gemini 2.5 Pro measured
FrontierCode	Not measured	40.1%	Only GLM 5.3 measured
FrontierSWE	Not measured	30.2%	Only GLM 5.3 measured
GSO-Bench	3.9%	Not measured	Only Gemini 2.5 Pro measured
WeirdML	54.0%	75.4%	GLM 5.3
ALE-Bench	Not measured	1317.4	Only GLM 5.3 measured
LMArena Text: coding	1452	1497	GLM 5.3
LMArena Text: software and IT	1459	1493	GLM 5.3
LMArena WebDev: React	Not measured	1641	Only GLM 5.3 measured
LMArena WebDev: HTML	1235	1595	GLM 5.3
LMArena WebDev: from an image	1285	Not measured	Only Gemini 2.5 Pro measured
Neither model has a result on the other 10 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02

Intelligence benchmarks for Gemini 2.5 Pro and GLM 5.3
Benchmark	Gemini 2.5 Pro	GLM 5.3	Leader
AA Intelligence Index	16.1	44.8	GLM 5.3
Epoch Capabilities Index	145.1	156.0	GLM 5.3

Agentic workLike-for-likePublic boards, shown as published, kept out of the index02

Agentic work benchmarks for Gemini 2.5 Pro and GLM 5.3
Benchmark	Gemini 2.5 Pro	GLM 5.3	Leader
AA Agentic Index	1.6	53.1	GLM 5.3
Design Arena: agents building mobile apps	Not measured	1194	Only GLM 5.3 measured
Design Arena: HTML slides	Not measured	1189	Only GLM 5.3 measured
Design Arena: PowerPoint slides	Not measured	1230	Only GLM 5.3 measured
LiveBench research	Not measured	73.2%	Only GLM 5.3 measured
LiveBench browser	Not measured	0.0%	Only GLM 5.3 measured
LiveBench long agentic	Not measured	35.9%	Only GLM 5.3 measured
SREBench (Vals AI)	Not measured	9.2%	Only GLM 5.3 measured
Finance Agent (Vals AI)	Not measured	55.8%	Only GLM 5.3 measured
Tax Agent Bench (Vals AI)	Not measured	39.7%	Only GLM 5.3 measured
Legal Research Bench (Vals AI)	Not measured	49.0%	Only GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)	Not measured	14.6%	Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)	Not measured	56.3%	Only GLM 5.3 measured
Terminal-Bench-Science (Vals AI)	Not measured	5.7%	Only GLM 5.3 measured
τ²-Bench Telecom	54.1%	Not measured	Only Gemini 2.5 Pro measured
APEX-Agents	Not measured	56.6%	Only GLM 5.3 measured
GDPval	23.3%	Not measured	Only Gemini 2.5 Pro measured
DeepResearch Bench	41.5%	Not measured	Only Gemini 2.5 Pro measured
Vending-Bench 2	$574	$8,164	GLM 5.3
BrowseComp-Plus	29.5%	Not measured	Only Gemini 2.5 Pro measured
Neither model has a result on the other 21 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index06

Reasoning benchmarks for Gemini 2.5 Pro and GLM 5.3
Benchmark	Gemini 2.5 Pro	GLM 5.3	Leader
GPQA Diamond	Not measured	88.1%	Only GLM 5.3 measured
LiveBench reasoning	Not measured	85.8%	Only GLM 5.3 measured
MysteryMechanism (Vals AI)	Not measured	23.0%	Only GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)	84.4%	91.7%	GLM 5.3
Humanity's Last Exam (Artificial Analysis run)	22.5%	42.3%	GLM 5.3
GPQA Diamond (Epoch AI run)	85.3%	90.9%	GLM 5.3
ARC-AGI	41.0%	Not measured	Only Gemini 2.5 Pro measured
ARC-AGI-2	4.9%	Not measured	Only Gemini 2.5 Pro measured
Chess puzzles	Not measured	37.0%	Only GLM 5.3 measured
Mystery game puzzles	Not measured	33.0%	Only GLM 5.3 measured
LMArena Text: hard prompts	1455	1490	GLM 5.3
LMArena Text: expert	1452	1514	GLM 5.3
LMArena Text: hard prompts in English	1453	1496	GLM 5.3
LMArena Vision: diagrams	1274	Not measured	Only Gemini 2.5 Pro measured
LMArena Vision: homework	1281	Not measured	Only Gemini 2.5 Pro measured
Kagi LLM Benchmark	70.3%	Not measured	Only Gemini 2.5 Pro measured
Neither model has a result on the other 6 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index05

Math benchmarks for Gemini 2.5 Pro and GLM 5.3
Benchmark	Gemini 2.5 Pro	GLM 5.3	Leader
AA Math Index	87.7	Not measured	Only Gemini 2.5 Pro measured
AIME 2025 (Artificial Analysis run)	87.7%	Not measured	Only Gemini 2.5 Pro measured
OTIS Mock AIME	84.7%	91.1%	GLM 5.3
FrontierMath, tiers 1 to 3	24.6%	68.8%	GLM 5.3
FrontierMath, tier 4	0.0%	29.3%	GLM 5.3
ProofBench	Not measured	49.0%	Only GLM 5.3 measured
LMArena Text: math	1450	1495	GLM 5.3
LMArena Text: mathematical work	1454	1497	GLM 5.3
Neither model has a result on the other 5 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index23

Knowledge benchmarks for Gemini 2.5 Pro and GLM 5.3
Benchmark	Gemini 2.5 Pro	GLM 5.3	Leader
MMLU Pro	Not measured	86.8%	Only GLM 5.3 measured
LegalBench (Vals AI)	Not measured	84.8%	Only GLM 5.3 measured
CaseLaw (Vals AI)	63.9%	Not measured	Only Gemini 2.5 Pro measured
CorpFin (Vals AI)	60.8%	Not measured	Only Gemini 2.5 Pro measured
TaxEval (Vals AI)	Not measured	72.4%	Only GLM 5.3 measured
MortgageTax (Vals AI)	68.9%	Not measured	Only Gemini 2.5 Pro measured
MedCode (Vals AI)	50.6%	42.9%	Gemini 2.5 Pro
MMLU Pro (Artificial Analysis run)	86.2%	Not measured	Only Gemini 2.5 Pro measured
SimpleQA Verified	Not measured	41.0%	Only GLM 5.3 measured
LMArena Text: science	1480	1498	GLM 5.3
LMArena Text: medicine and healthcare	1476	1481	GLM 5.3
LMArena Text: legal and government	1480	1473	Gemini 2.5 Pro
LMArena Text: business and finance	1439	1459	GLM 5.3
FACTS Benchmark Suite	61.5%	Not measured	Only Gemini 2.5 Pro measured
Neither model has a result on the other 4 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index06

Web design benchmarks for Gemini 2.5 Pro and GLM 5.3
Benchmark	Gemini 2.5 Pro	GLM 5.3	Leader
Design Arena: websites	1167	1295	GLM 5.3
Design Arena: UI components	1137	1314	GLM 5.3
Design Arena: game dev	1111	1311	GLM 5.3
Design Arena: data viz	1222	1260	GLM 5.3
Design Arena: 3D	1080	1332	GLM 5.3
Design Arena: all code categories	1152	1304	GLM 5.3
Design Arena: SVG	Not measured	1294	Only GLM 5.3 measured
Design Arena: ASCII art	Not measured	1208	Only GLM 5.3 measured
LiveBench design	Not measured	77.3%	Only GLM 5.3 measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index318

General benchmarks for Gemini 2.5 Pro and GLM 5.3
Benchmark	Gemini 2.5 Pro	GLM 5.3	Leader
Vals Index	Not measured	59.7%	Only GLM 5.3 measured
LiveBench data analysis	Not measured	70.2%	Only GLM 5.3 measured
Public Benefits Bench (Vals AI)	Not measured	68.5%	Only GLM 5.3 measured
SAGE (Vals AI)	41.9%	Not measured	Only Gemini 2.5 Pro measured
MedScribe (Vals AI)	73.5%	88.8%	GLM 5.3
IFBench	48.7%	Not measured	Only Gemini 2.5 Pro measured
Long context reasoning (AA-LCR)	69.0%	79.7%	GLM 5.3
DTBench	82.4%	87.7%	GLM 5.3
LMCA	34.8%	55.5%	GLM 5.3
Creative writing (Lech Mazur)	83.8%	Not measured	Only Gemini 2.5 Pro measured
LMArena Text	1458	1474	GLM 5.3
LMArena Text: instruction following	1437	1479	GLM 5.3
LMArena Text: creative writing	1454	1458	GLM 5.3
LMArena Vision	1263	Not measured	Only Gemini 2.5 Pro measured
LMArena Text: English	1458	1482	GLM 5.3
LMArena Text: non-English	1452	1459	GLM 5.3
LMArena Text: Chinese	1507	1532	GLM 5.3
LMArena Text: German	1487	1488	GLM 5.3
LMArena Text: Japanese	1461	1460	Gemini 2.5 Pro
LMArena Text: Korean	1434	1460	GLM 5.3
LMArena Text: French	1473	1503	GLM 5.3
LMArena Text: Spanish	1474	1462	Gemini 2.5 Pro
LMArena Text: Russian	1461	1469	GLM 5.3
LMArena Text: Polish	1479	1466	Gemini 2.5 Pro
LMArena Text: multi-turn	1453	1472	GLM 5.3
LMArena Text: longer queries	1449	1483	GLM 5.3
LMArena Text: writing and language	1454	1469	GLM 5.3
LMArena Text: entertainment and media	1441	1447	GLM 5.3
LMArena Vision: English	1261	Not measured	Only Gemini 2.5 Pro measured
LMArena Vision: Chinese	1300	Not measured	Only Gemini 2.5 Pro measured
LMArena Vision: reading text	1270	Not measured	Only Gemini 2.5 Pro measured
LMArena Vision: humor	1262	Not measured	Only Gemini 2.5 Pro measured
LMArena Vision: captioning	1304	Not measured	Only Gemini 2.5 Pro measured
LMArena Vision: recognizing things	1283	Not measured	Only Gemini 2.5 Pro measured
LMArena Vision: creative writing	1265	Not measured	Only Gemini 2.5 Pro measured
LMArena Document	1421	Not measured	Only Gemini 2.5 Pro measured
Neither model has a result on the other 10 boards here

Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Image generation benchmarks for Gemini 2.5 Pro and GLM 5.3
Benchmark	Gemini 2.5 Pro	GLM 5.3	Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Gemini 2.5 Pro$0.0063Fits in one request

GLM 5.3$0.0036Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Pro$0.092Fits in one request

GLM 5.3$0.083Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Pro$0.350Fits in one request

GLM 5.3$0.324Fits in one request

GLM 5.3 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Gemini 2.5 Pro and GLM 5.3
Specification	Google Gemini 2.5 Pro	Z.ai GLM 5.3
Maker	Google	Z.ai
Weights	No public weights listed	Open weights
Released	June 17, 2025	August 18, 2026
Context window	1.05M	1.05M
Input $/Mtok	$1.25	$1.40
Output $/Mtok	$10.00	$4.40
Coding index	19.1	70.6
Independent measurements behind it	3	6
Cost per solved task	$0.64	$0.36
Source	OpenRouter listing	OpenRouter listing

Questions

Is Gemini 2.5 Pro better than GLM 5.3 for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro. Pick Gemini 2.5 Pro for work measured by MedCode (Vals AI) or LMArena Text: legal and government, where it leads.

Which is cheaper, Gemini 2.5 Pro or GLM 5.3?

Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Per solved SWE-bench Verified task, Gemini 2.5 Pro costs $0.64 and GLM 5.3 $0.36.

Which has the bigger context window, Gemini 2.5 Pro or GLM 5.3?

Neither: both take 1.05M tokens.

How many benchmarks is the Gemini 2.5 Pro vs GLM 5.3 comparison based on?

56 benchmarks have a result for both models as of October 22, 2026. Gemini 2.5 Pro's coding index rests on 3 independent benchmarks and GLM 5.3's on 6. Where only one model has a result, the page says so instead of naming a winner.

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Gemini 2.5 Pro or GLM 5.3: which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Knowledge

Web design

General

Long documents

Everyday cost

Cost per solved task

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

Compare next